A Paper Currency Recognition System based on Neural Networks with Gaussian Functions and an Optimizing Method for Its Parameters on Way to Learning

Abstract

In this paper, in order to improve rejection capabilities of the paper currency recognition system for unknown currency patterns on promise of ensuring recognition capabilities for known currency patterns, a feed-forward neural network (FNN) with Gaussian activation function is proposed. The proposed activation function is a ridge-like function. Moreover, a hybrid-learning algorithm for optimizing the width parameters of the Gaussian function is proposed. In the network the Gaussian activation function instead of the sigmoid function is employed in all units of hidden and output layers. The algorithm consists of two steps, one is exploring local minima by employing the gradient descent search, and the other is extricating the search from local minima, in which a random search with the downhill simplex method is employed. The results of simulation reveal the potential effectiveness of the proposed activation function and the algorithm. The system with the proposed activation function and the proposed algorithm can recognize known currency patterns and reject the unknown currency patterns effectively.